Overview

Dataset statistics

Number of variables18
Number of observations10497
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory144.0 B

Variable types

Numeric10
Boolean3
Categorical5

Alerts

df_index is highly correlated with UserID and 1 other fieldsHigh correlation
UserID is highly correlated with df_index and 1 other fieldsHigh correlation
Yearly_avg_view_on_travel_page is highly correlated with Daily_Avg_mins_spend_on_traveling_pageHigh correlation
total_likes_on_outofstation_checkin_received is highly correlated with Daily_Avg_mins_spend_on_traveling_pageHigh correlation
montly_avg_comment_on_company_page is highly correlated with df_index and 1 other fieldsHigh correlation
Daily_Avg_mins_spend_on_traveling_page is highly correlated with Yearly_avg_view_on_travel_page and 1 other fieldsHigh correlation
df_index is highly correlated with UserIDHigh correlation
UserID is highly correlated with df_indexHigh correlation
Yearly_avg_view_on_travel_page is highly correlated with total_likes_on_outofstation_checkin_received and 1 other fieldsHigh correlation
total_likes_on_outofstation_checkin_received is highly correlated with Yearly_avg_view_on_travel_page and 1 other fieldsHigh correlation
Daily_Avg_mins_spend_on_traveling_page is highly correlated with Yearly_avg_view_on_travel_page and 1 other fieldsHigh correlation
df_index is highly correlated with UserIDHigh correlation
UserID is highly correlated with df_indexHigh correlation
df_index is highly correlated with UserID and 2 other fieldsHigh correlation
UserID is highly correlated with df_index and 2 other fieldsHigh correlation
yearly_avg_Outstation_checkins is highly correlated with df_index and 2 other fieldsHigh correlation
preferred_location_type is highly correlated with df_index and 2 other fieldsHigh correlation
total_likes_on_outofstation_checkin_received is highly correlated with Daily_Avg_mins_spend_on_traveling_pageHigh correlation
Daily_Avg_mins_spend_on_traveling_page is highly correlated with total_likes_on_outofstation_checkin_receivedHigh correlation
df_index has unique values Unique
UserID has unique values Unique
week_since_last_outstation_checkin has 917 (8.7%) zeros Zeros

Reproduction

Analysis started2022-04-30 06:06:31.344711
Analysis finished2022-04-30 06:06:53.913180
Duration22.57 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10497
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6265.048204
Minimum0
Maximum11759
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-04-30T11:36:54.028177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile706.8
Q13435
median6511
Q39135
95-th percentile11234.2
Maximum11759
Range11759
Interquartile range (IQR)5700

Descriptive statistics

Standard deviation3345.51008
Coefficient of variation (CV)0.533995904
Kurtosis-1.138928624
Mean6265.048204
Median Absolute Deviation (MAD)2822
Skewness-0.1579051057
Sum65764211
Variance11192437.69
MonotonicityStrictly increasing
2022-04-30T11:36:54.221181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
7571
 
< 0.1%
48551
 
< 0.1%
110001
 
< 0.1%
89531
 
< 0.1%
28121
 
< 0.1%
7651
 
< 0.1%
69101
 
< 0.1%
48631
 
< 0.1%
110081
 
< 0.1%
Other values (10487)10487
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
81
< 0.1%
101
< 0.1%
111
< 0.1%
121
< 0.1%
ValueCountFrequency (%)
117591
< 0.1%
117581
< 0.1%
117571
< 0.1%
117561
< 0.1%
117551
< 0.1%
117541
< 0.1%
117531
< 0.1%
117521
< 0.1%
117511
< 0.1%
117501
< 0.1%

UserID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10497
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1006266.048
Minimum1000001
Maximum1011760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-04-30T11:36:54.417139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1000001
5-th percentile1000707.8
Q11003436
median1006512
Q31009136
95-th percentile1011235.2
Maximum1011760
Range11759
Interquartile range (IQR)5700

Descriptive statistics

Standard deviation3345.51008
Coefficient of variation (CV)0.00332467749
Kurtosis-1.138928624
Mean1006266.048
Median Absolute Deviation (MAD)2822
Skewness-0.1579051057
Sum1.056277471 × 1010
Variance11192437.69
MonotonicityStrictly increasing
2022-04-30T11:36:54.628144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10014731
 
< 0.1%
10044071
 
< 0.1%
10085051
 
< 0.1%
10023641
 
< 0.1%
10003171
 
< 0.1%
10064621
 
< 0.1%
10105601
 
< 0.1%
10085131
 
< 0.1%
10023721
 
< 0.1%
10003251
 
< 0.1%
Other values (10487)10487
99.9%
ValueCountFrequency (%)
10000011
< 0.1%
10000021
< 0.1%
10000031
< 0.1%
10000041
< 0.1%
10000051
< 0.1%
10000061
< 0.1%
10000091
< 0.1%
10000111
< 0.1%
10000121
< 0.1%
10000131
< 0.1%
ValueCountFrequency (%)
10117601
< 0.1%
10117591
< 0.1%
10117581
< 0.1%
10117571
< 0.1%
10117561
< 0.1%
10117551
< 0.1%
10117541
< 0.1%
10117531
< 0.1%
10117521
< 0.1%
10117511
< 0.1%

Buy_ticket
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
False
8795 
True
1702 
ValueCountFrequency (%)
False8795
83.8%
True1702
 
16.2%
2022-04-30T11:36:54.772177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Yearly_avg_view_on_travel_page
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct331
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.2457845
Minimum35
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-04-30T11:36:54.902177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile182
Q1232
median271
Q3325
95-th percentile410
Maximum464
Range429
Interquartile range (IQR)93

Descriptive statistics

Standard deviation68.28073068
Coefficient of variation (CV)0.2427795702
Kurtosis-0.3072463864
Mean281.2457845
Median Absolute Deviation (MAD)45
Skewness0.4081016958
Sum2952237
Variance4662.258182
MonotonicityNot monotonic
2022-04-30T11:36:55.079177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255169
 
1.6%
262163
 
1.6%
270160
 
1.5%
217137
 
1.3%
232137
 
1.3%
247123
 
1.2%
240120
 
1.1%
225118
 
1.1%
285117
 
1.1%
264116
 
1.1%
Other values (321)9137
87.0%
ValueCountFrequency (%)
354
< 0.1%
424
< 0.1%
1353
 
< 0.1%
1368
0.1%
1376
0.1%
1383
 
< 0.1%
1402
 
< 0.1%
1413
 
< 0.1%
1424
< 0.1%
1437
0.1%
ValueCountFrequency (%)
4641
 
< 0.1%
4631
 
< 0.1%
4622
 
< 0.1%
4612
 
< 0.1%
4603
< 0.1%
4592
 
< 0.1%
4581
 
< 0.1%
4573
< 0.1%
4565
< 0.1%
4557
0.1%

preferred_device
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
Mobile
9389 
Laptop
1108 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile
2nd rowMobile
3rd rowMobile
4th rowMobile
5th rowMobile

Common Values

ValueCountFrequency (%)
Mobile9389
89.4%
Laptop1108
 
10.6%

Length

2022-04-30T11:36:55.258179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T11:36:55.357142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mobile9389
89.4%
laptop1108
 
10.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct7566
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28169.87673
Minimum3570
Maximum152465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-04-30T11:36:55.483177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3570
5-th percentile5748
Q116321
median28178
Q340529
95-th percentile49881.2
Maximum152465
Range148895
Interquartile range (IQR)24208

Descriptive statistics

Standard deviation14207.22205
Coefficient of variation (CV)0.504340938
Kurtosis-0.1348271478
Mean28169.87673
Median Absolute Deviation (MAD)12007
Skewness0.1112174145
Sum295699196
Variance201845158.4
MonotonicityNot monotonic
2022-04-30T11:36:55.673177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1151511
 
0.1%
185509
 
0.1%
241859
 
0.1%
378709
 
0.1%
449058
 
0.1%
401108
 
0.1%
341957
 
0.1%
70707
 
0.1%
122507
 
0.1%
162757
 
0.1%
Other values (7556)10415
99.2%
ValueCountFrequency (%)
35702
< 0.1%
35771
< 0.1%
35781
< 0.1%
36052
< 0.1%
36111
< 0.1%
36141
< 0.1%
36181
< 0.1%
36201
< 0.1%
36211
< 0.1%
36311
< 0.1%
ValueCountFrequency (%)
1524651
< 0.1%
1524301
< 0.1%
525121
< 0.1%
525091
< 0.1%
524981
< 0.1%
524951
< 0.1%
524871
< 0.1%
524791
< 0.1%
524741
< 0.1%
524691
< 0.1%

yearly_avg_Outstation_checkins
Categorical

HIGH CORRELATION

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
1
3794 
2
844 
10
617 
9
 
340
3
 
336
Other values (25)
4566 

Length

Max length2
Median length1
Mean length1.359436029
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
13794
36.1%
2844
 
8.0%
10617
 
5.9%
9340
 
3.2%
3336
 
3.2%
7336
 
3.2%
8320
 
3.0%
5261
 
2.5%
4256
 
2.4%
6236
 
2.2%
Other values (20)3157
30.1%

Length

2022-04-30T11:36:55.888180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13794
36.1%
2844
 
8.0%
10617
 
5.9%
9340
 
3.2%
3336
 
3.2%
7336
 
3.2%
8320
 
3.0%
5261
 
2.5%
4256
 
2.4%
6236
 
2.2%
Other values (20)3157
30.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

member_in_family
Real number (ℝ≥0)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.924930933
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-04-30T11:36:56.036177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.042121903
Coefficient of variation (CV)0.3562894056
Kurtosis1.278071422
Mean2.924930933
Median Absolute Deviation (MAD)1
Skewness-0.003406428921
Sum30703
Variance1.086018062
MonotonicityNot monotonic
2022-04-30T11:36:56.168178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
34109
39.1%
42859
27.2%
21989
18.9%
11197
 
11.4%
5333
 
3.2%
1010
 
0.1%
ValueCountFrequency (%)
11197
 
11.4%
21989
18.9%
34109
39.1%
42859
27.2%
5333
 
3.2%
1010
 
0.1%
ValueCountFrequency (%)
1010
 
0.1%
5333
 
3.2%
42859
27.2%
34109
39.1%
21989
18.9%
11197
 
11.4%

preferred_location_type
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
Beach
2424 
Financial
1894 
Historical site
1856 
Medical
1463 
Other
1307 
Other values (2)
1553 

Length

Max length15
Median length8
Mean length8.649233114
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFinancial
2nd rowFinancial
3rd rowOther
4th rowFinancial
5th rowMedical

Common Values

ValueCountFrequency (%)
Beach2424
23.1%
Financial1894
18.0%
Historical site1856
17.7%
Medical1463
13.9%
Other1307
12.5%
Entertainment917
 
8.7%
Trekking636
 
6.1%

Length

2022-04-30T11:36:56.334177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T11:36:56.564177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
beach2424
19.6%
financial1894
15.3%
historical1856
15.0%
site1856
15.0%
medical1463
11.8%
other1307
10.6%
entertainment917
 
7.4%
trekking636
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct99
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.12651234
Minimum3
Maximum685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-04-30T11:36:56.781141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile41
Q157
median75
Q393
95-th percentile109
Maximum685
Range682
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.19647818
Coefficient of variation (CV)0.3087655404
Kurtosis72.38135158
Mean75.12651234
Median Absolute Deviation (MAD)18
Skewness2.872399338
Sum788603
Variance538.0765998
MonotonicityNot monotonic
2022-04-30T11:36:56.970179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96175
 
1.7%
90173
 
1.6%
66171
 
1.6%
56169
 
1.6%
72167
 
1.6%
80166
 
1.6%
60163
 
1.6%
91161
 
1.5%
87160
 
1.5%
95159
 
1.5%
Other values (89)8833
84.1%
ValueCountFrequency (%)
329
0.3%
3124
 
0.2%
3243
0.4%
3334
0.3%
3435
0.3%
3539
0.4%
3649
0.5%
3744
0.4%
3860
0.6%
3957
0.5%
ValueCountFrequency (%)
6851
 
< 0.1%
6151
 
< 0.1%
2151
 
< 0.1%
1257
 
0.1%
1243
 
< 0.1%
1238
 
0.1%
12210
0.1%
12111
0.1%
12010
0.1%
11924
0.2%

total_likes_on_outofstation_checkin_received
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5980
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6537.784034
Minimum1009
Maximum20065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-04-30T11:36:57.181145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2136
Q12947
median4948
Q38398
95-th percentile17871.4
Maximum20065
Range19056
Interquartile range (IQR)5451

Descriptive statistics

Standard deviation4708.251662
Coefficient of variation (CV)0.7201601702
Kurtosis1.005578467
Mean6537.784034
Median Absolute Deviation (MAD)2192
Skewness1.37044156
Sum68627119
Variance22167633.72
MonotonicityNot monotonic
2022-04-30T11:36:57.367142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237711
 
0.1%
24379
 
0.1%
24049
 
0.1%
23429
 
0.1%
23808
 
0.1%
24518
 
0.1%
25708
 
0.1%
20968
 
0.1%
27938
 
0.1%
23878
 
0.1%
Other values (5970)10411
99.2%
ValueCountFrequency (%)
10092
< 0.1%
10141
< 0.1%
10171
< 0.1%
10501
< 0.1%
10511
< 0.1%
10522
< 0.1%
10551
< 0.1%
10581
< 0.1%
10601
< 0.1%
10612
< 0.1%
ValueCountFrequency (%)
200651
< 0.1%
200561
< 0.1%
200381
< 0.1%
200361
< 0.1%
200321
< 0.1%
200301
< 0.1%
200081
< 0.1%
200041
< 0.1%
200001
< 0.1%
199991
< 0.1%

week_since_last_outstation_checkin
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.20777365
Minimum0
Maximum11
Zeros917
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-04-30T11:36:57.546179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.614122455
Coefficient of variation (CV)0.8149335772
Kurtosis-0.04154014365
Mean3.20777365
Median Absolute Deviation (MAD)2
Skewness0.91199506
Sum33672
Variance6.833636211
MonotonicityNot monotonic
2022-04-30T11:36:57.677142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12733
26.0%
31605
15.3%
21503
14.3%
4993
 
9.5%
0917
 
8.7%
5643
 
6.1%
6585
 
5.6%
7545
 
5.2%
9410
 
3.9%
8384
 
3.7%
Other values (2)179
 
1.7%
ValueCountFrequency (%)
0917
 
8.7%
12733
26.0%
21503
14.3%
31605
15.3%
4993
 
9.5%
5643
 
6.1%
6585
 
5.6%
7545
 
5.2%
8384
 
3.7%
9410
 
3.9%
ValueCountFrequency (%)
1154
 
0.5%
10125
 
1.2%
9410
 
3.9%
8384
 
3.7%
7545
 
5.2%
6585
 
5.6%
5643
6.1%
4993
9.5%
31605
15.3%
21503
14.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
False
7549 
True
2948 
ValueCountFrequency (%)
False7549
71.9%
True2948
 
28.1%
2022-04-30T11:36:57.783177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

montly_avg_comment_on_company_page
Real number (ℝ≥0)

HIGH CORRELATION

Distinct149
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.86986758
Minimum11
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-04-30T11:36:57.905181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12
Q118
median23
Q328
95-th percentile37
Maximum499
Range488
Interquartile range (IQR)10

Descriptive statistics

Standard deviation47.53377955
Coefficient of variation (CV)1.646484156
Kurtosis62.97381813
Mean28.86986758
Median Absolute Deviation (MAD)5
Skewness7.883086906
Sum303047
Variance2259.460198
MonotonicityNot monotonic
2022-04-30T11:36:58.099179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23620
 
5.9%
22574
 
5.5%
24552
 
5.3%
25548
 
5.2%
20527
 
5.0%
21520
 
5.0%
19500
 
4.8%
18494
 
4.7%
26476
 
4.5%
17439
 
4.2%
Other values (139)5247
50.0%
ValueCountFrequency (%)
11321
3.1%
12325
3.1%
13312
3.0%
14379
3.6%
15301
2.9%
16346
3.3%
17439
4.2%
18494
4.7%
19500
4.8%
20527
5.0%
ValueCountFrequency (%)
4991
< 0.1%
4971
< 0.1%
4911
< 0.1%
4902
< 0.1%
4881
< 0.1%
4871
< 0.1%
4861
< 0.1%
4842
< 0.1%
4832
< 0.1%
4782
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
False
8884 
True
1613 
ValueCountFrequency (%)
False8884
84.6%
True1613
 
15.4%
2022-04-30T11:36:58.230176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
3
3309 
4
3073 
2
2157 
1
1958 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
33309
31.5%
43073
29.3%
22157
20.5%
11958
18.7%

Length

2022-04-30T11:36:58.344177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T11:36:58.448144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
33309
31.5%
43073
29.3%
22157
20.5%
11958
18.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

number_of_adults
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
0
4497 
1
4266 
2
1115 
3
619 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
04497
42.8%
14266
40.6%
21115
 
10.6%
3619
 
5.9%

Length

2022-04-30T11:36:58.596177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T11:36:58.700179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04497
42.8%
14266
40.6%
21115
 
10.6%
3619
 
5.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Daily_Avg_mins_spend_on_traveling_page
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.84471754
Minimum0
Maximum235
Zeros36
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-04-30T11:36:58.851178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median12
Q318
95-th percentile31
Maximum235
Range235
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.822850529
Coefficient of variation (CV)0.6372719779
Kurtosis50.24238823
Mean13.84471754
Median Absolute Deviation (MAD)5
Skewness3.040138233
Sum145328
Variance77.84269146
MonotonicityNot monotonic
2022-04-30T11:36:59.032177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10995
 
9.5%
9599
 
5.7%
8585
 
5.6%
6558
 
5.3%
7488
 
4.6%
11475
 
4.5%
13474
 
4.5%
14448
 
4.3%
12441
 
4.2%
15427
 
4.1%
Other values (41)5007
47.7%
ValueCountFrequency (%)
036
 
0.3%
1296
2.8%
2130
 
1.2%
3195
 
1.9%
4304
2.9%
5390
3.7%
6558
5.3%
7488
4.6%
8585
5.6%
9599
5.7%
ValueCountFrequency (%)
2351
 
< 0.1%
1701
 
< 0.1%
1351
 
< 0.1%
471
 
< 0.1%
463
 
< 0.1%
454
 
< 0.1%
447
 
0.1%
434
 
< 0.1%
425
 
< 0.1%
4119
0.2%

Interactions

2022-04-30T11:36:50.951177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:33.062140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:35.117138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:37.150177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:38.960182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:41.144178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:42.945181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:44.963139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:47.138141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:49.044142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:51.140180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:33.256177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:35.321176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:37.327141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:39.169141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:41.328177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:43.150164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:45.171180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:47.325177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:49.238178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:51.334179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:33.457140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:35.528177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:37.515140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:39.380178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:41.516181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:43.360143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:45.389177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:47.524177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:49.438181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:51.505177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:33.643172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:35.714178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:37.687141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:39.569140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:41.679179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:43.543177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:45.576177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:47.695177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:49.611180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:51.710177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:33.863180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:35.937140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:37.896180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:39.797140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:41.874181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:43.764144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:45.801142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:47.909145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:49.825177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:51.866183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:34.030142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:36.123138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:38.048177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:39.979142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:42.029140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:43.943165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:45.984141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:48.072142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:49.990141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:52.061179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:34.239177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:36.344177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:38.239180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:40.198145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:42.222177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:44.155142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:46.203140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:48.273165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:50.191138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:52.263177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:34.445177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:36.562183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:38.437141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:40.420177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:42.422184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:44.376177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:46.424141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:48.484139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:50.400178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:52.444177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:34.749141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:36.763145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:38.612180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:40.623142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:42.597141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:44.577141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:46.623177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:48.671164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:50.583178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:52.629141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:34.936177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:36.958177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:38.788180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:40.945181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:42.778184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:44.774177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:46.942183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:48.858141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-30T11:36:50.770178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-04-30T11:36:59.207141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-30T11:36:59.596179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-30T11:37:00.096144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-30T11:37:00.465178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-30T11:37:00.761177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-30T11:36:53.073144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-30T11:36:53.657141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexUserIDBuy_ticketYearly_avg_view_on_travel_pagepreferred_devicetotal_likes_on_outstation_checkin_givenyearly_avg_Outstation_checkinsmember_in_familypreferred_location_typeYearly_avg_comment_on_travel_pagetotal_likes_on_outofstation_checkin_receivedweek_since_last_outstation_checkinfollowing_company_pagemontly_avg_comment_on_company_pageworking_flagtravelling_network_ratingnumber_of_adultsDaily_Avg_mins_spend_on_traveling_page
001000001Yes307.0Mobile38570.012Financial94.059938Yes11No108
111000002No367.0Mobile9765.011Financial61.051301No23Yes4110
221000003Yes277.0Mobile48055.012Other92.020906Yes15No207
331000004No247.0Mobile48720.014Financial56.029091Yes11No308
441000005No202.0Mobile20685.011Medical40.034689No12No416
551000006No240.0Mobile35175.012Financial79.030680No13No308
681000009No285.0Mobile7560.0233Financial44.095260No21Yes2010
7101000011No262.0Mobile28315.0163Medical84.024260No13No316
8111000012No217.0Mobile5355.0152Financial49.041930Yes12No4010
9121000013No232.0Mobile23450.0261Financial31.029111No17No415

Last rows

df_indexUserIDBuy_ticketYearly_avg_view_on_travel_pagepreferred_devicetotal_likes_on_outstation_checkin_givenyearly_avg_Outstation_checkinsmember_in_familypreferred_location_typeYearly_avg_comment_on_travel_pagetotal_likes_on_outofstation_checkin_receivedweek_since_last_outstation_checkinfollowing_company_pagemontly_avg_comment_on_company_pageworking_flagtravelling_network_ratingnumber_of_adultsDaily_Avg_mins_spend_on_traveling_page
10487117501011751No231.0Mobile16423.0284Historical site96.038451No26No2012
10488117511011752Yes383.0Mobile14399.0283Other58.0109106Yes28No2123
10489117521011753No302.0Mobile25317.0241Other79.0120930No24No1129
10490117531011754No247.0Mobile11418.053Historical site99.099831No28No2016
10491117541011755No210.0Mobile40886.053Other53.030242No32No4014
10492117551011756No279.0Laptop30987.0232Historical site58.026164No36No3123
10493117561011757No305.0Mobile21510.061Historical site55.0100414No30No1111
10494117571011758No214.0Mobile5478.043Beach103.062033Yes40Yes2112
10495117581011759No382.0Laptop35851.023Historical site83.054443No32No4020
10496117591011760No270.0Mobile22025.083Historical site104.044702No29No1014